Using genetic algorithms to uncover individual differences in how humans represent facial emotion. / Carlisi, Christina; Reed, Kyle; Helmink, Fleur; Lachlan, Robert; Cosker, Darren; Viding, Essi; Mareschal, Isabelle.

In: Royal Society Open Science, Vol. 8, No. 10, 13.10.2021.

Research output: Contribution to journalArticlepeer-review

Published

Standard

Using genetic algorithms to uncover individual differences in how humans represent facial emotion. / Carlisi, Christina; Reed, Kyle; Helmink, Fleur; Lachlan, Robert; Cosker, Darren; Viding, Essi; Mareschal, Isabelle.

In: Royal Society Open Science, Vol. 8, No. 10, 13.10.2021.

Research output: Contribution to journalArticlepeer-review

Harvard

Carlisi, C, Reed, K, Helmink, F, Lachlan, R, Cosker, D, Viding, E & Mareschal, I 2021, 'Using genetic algorithms to uncover individual differences in how humans represent facial emotion', Royal Society Open Science, vol. 8, no. 10. https://doi.org/10.1098/rsos.202251

APA

Carlisi, C., Reed, K., Helmink, F., Lachlan, R., Cosker, D., Viding, E., & Mareschal, I. (2021). Using genetic algorithms to uncover individual differences in how humans represent facial emotion. Royal Society Open Science, 8(10). https://doi.org/10.1098/rsos.202251

Vancouver

Carlisi C, Reed K, Helmink F, Lachlan R, Cosker D, Viding E et al. Using genetic algorithms to uncover individual differences in how humans represent facial emotion. Royal Society Open Science. 2021 Oct 13;8(10). https://doi.org/10.1098/rsos.202251

Author

Carlisi, Christina ; Reed, Kyle ; Helmink, Fleur ; Lachlan, Robert ; Cosker, Darren ; Viding, Essi ; Mareschal, Isabelle. / Using genetic algorithms to uncover individual differences in how humans represent facial emotion. In: Royal Society Open Science. 2021 ; Vol. 8, No. 10.

BibTeX

@article{6c90ec3a13e94e128d863dd3a5acd507,
title = "Using genetic algorithms to uncover individual differences in how humans represent facial emotion",
abstract = "Emotional facial expressions critically impact social interactions and cognition. However, emotion research to date has generally relied on the assumption that people represent categorical emotions in the same way, using standardized stimulus sets and overlooking important individual differences. To resolve this problem, we developed and tested a task using genetic algorithms to derive assumption-free, participant-generated emotional expressions. One hundred and five participants generated a subjective representation of happy, angry, fearful and sad faces. Population-level consistency was observed for happy faces, but fearful and sad faces showed a high degree of variability. High test–retest reliability was observed across all emotions. A separate group of 108 individuals accurately identified happy and angry faces from the first study, while fearful and sad faces were commonly misidentified. These findings are an important first step towards understanding individual differences in emotion representation, with the potential to reconceptualize the way we study atypical emotion processing in future research.",
author = "Christina Carlisi and Kyle Reed and Fleur Helmink and Robert Lachlan and Darren Cosker and Essi Viding and Isabelle Mareschal",
year = "2021",
month = oct,
day = "13",
doi = "10.1098/rsos.202251",
language = "English",
volume = "8",
journal = "Royal Society Open Science",
issn = "2054-5703",
publisher = "The Royal Society",
number = "10",

}

RIS

TY - JOUR

T1 - Using genetic algorithms to uncover individual differences in how humans represent facial emotion

AU - Carlisi, Christina

AU - Reed, Kyle

AU - Helmink, Fleur

AU - Lachlan, Robert

AU - Cosker, Darren

AU - Viding, Essi

AU - Mareschal, Isabelle

PY - 2021/10/13

Y1 - 2021/10/13

N2 - Emotional facial expressions critically impact social interactions and cognition. However, emotion research to date has generally relied on the assumption that people represent categorical emotions in the same way, using standardized stimulus sets and overlooking important individual differences. To resolve this problem, we developed and tested a task using genetic algorithms to derive assumption-free, participant-generated emotional expressions. One hundred and five participants generated a subjective representation of happy, angry, fearful and sad faces. Population-level consistency was observed for happy faces, but fearful and sad faces showed a high degree of variability. High test–retest reliability was observed across all emotions. A separate group of 108 individuals accurately identified happy and angry faces from the first study, while fearful and sad faces were commonly misidentified. These findings are an important first step towards understanding individual differences in emotion representation, with the potential to reconceptualize the way we study atypical emotion processing in future research.

AB - Emotional facial expressions critically impact social interactions and cognition. However, emotion research to date has generally relied on the assumption that people represent categorical emotions in the same way, using standardized stimulus sets and overlooking important individual differences. To resolve this problem, we developed and tested a task using genetic algorithms to derive assumption-free, participant-generated emotional expressions. One hundred and five participants generated a subjective representation of happy, angry, fearful and sad faces. Population-level consistency was observed for happy faces, but fearful and sad faces showed a high degree of variability. High test–retest reliability was observed across all emotions. A separate group of 108 individuals accurately identified happy and angry faces from the first study, while fearful and sad faces were commonly misidentified. These findings are an important first step towards understanding individual differences in emotion representation, with the potential to reconceptualize the way we study atypical emotion processing in future research.

U2 - 10.1098/rsos.202251

DO - 10.1098/rsos.202251

M3 - Article

VL - 8

JO - Royal Society Open Science

JF - Royal Society Open Science

SN - 2054-5703

IS - 10

ER -